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Global Optimization of Bioprocesses using Stochastic and Hybrid Methods

  • Conference paper
Frontiers in Global Optimization

Part of the book series: Nonconvex Optimization and Its Applications ((NOIA,volume 74))

Abstract

In this contribution, we will focus on problems arising in the context of biochemical process engineering. Many of these problems can be stated as the optimization of non-linear dynamic systems. Relevant classes in this domain are (i) optimal control problems (dynamic optimization), (ii) inverse problems (parameter estimation), and (iii) simultaneous design and control optimization problems. Most of these problems are, or can be transformed to, nonlinear programming problems subject to differential-algebraic constraints. It should be noted that their highly constrained and non-linear nature often causes non-convexity, thus global optimization methods are needed to find suitable solutions.

Here, we will present our experiences regarding the use of several stochastic, deterministic and hybrid global optimization methods to solve those problems. Several parallel versions of the most promising methods, which are able to run on standard clusters of PCs, will also be presented. Results for selected challenging case studies will be given.

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Abbreviations

ACO:

ant colony optimization

BVP:

boundary value problem

CP:

complete (state and control) parameterization

CVP:

control vector parameterization

DAEs:

differential algebraic equations

DE:

differential evolution

EC:

evolutionary computation

EP:

evolutionary programming

ES:

evolution strategy

GA:

genetic algorithm

ICRS:

Integrated Controlled Random Search

ISE:

integral square error

LJ:

Luus-Jaakola

MIOCP:

mixed integer optimal control problem

NFL:

no free lunch (theorem)

NLP:

nonlinear programming

ODEs:

ordinary differential equations

PDE:

partial differential equation

PI:

proportional integral (controller)

SA:

simulated annealing

SQP:

sequential quadratic programming

SRES:

Stochastic Ranking Evolution Strategy

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Authors and Affiliations

  1. Process Engineering Group, IIM-CSIC, C/Eduardo Cabello 6, 36208, Vigo, Spain

    Julio R. Banga, Carmen G. Moles & Antonio A. Alonso

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  1. Julio R. Banga
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  2. Carmen G. Moles
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Editors and Affiliations

  1. Princeton University, USA

    C. A. Floudas

  2. University of Florida, USA

    Panos Pardalos

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Banga, J.R., Moles, C.G., Alonso, A.A. (2004). Global Optimization of Bioprocesses using Stochastic and Hybrid Methods. In: Floudas, C.A., Pardalos, P. (eds) Frontiers in Global Optimization. Nonconvex Optimization and Its Applications, vol 74. Springer, Boston, MA. https://doi.org/10.1007/978-1-4613-0251-3_3

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